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Technical Staff Member – Agent
Company WatchEngineering-focused role developing scalable systems for AI research at H. Collaborate in pushing the frontiers of agentic AI within a dynamic team environment.
Tech Stack
Tools & technologiesAWSCloudDistributed Systems
About the role
Key responsibilities & impact- Design and develop new agents, proposing new research directions, e.g., combining state-of-the-art RL with foundation models (LLMs/VLMs).
- Design, implement, and scale complex, high-performance systems for training large-scale agents. This includes both the foundational infrastructure and the novel algorithms, reward models, and sophisticated training environments.
- Collaborate closely with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures.
- Create, manage, and scale massive benchmarks and evaluation systems to rigorously track agent capabilities. You will own system reliability, scalability, and observability for our entire research infrastructure.
- Mentor and guide other engineers and researchers on the team, fostering technical excellence. You will establish and enforce engineering standards, tooling, and best practices for both code and research design.
- Conduct thorough code and design reviews, champion technical innovation, and proactively address technical debt to accelerate the R&D lifecycle.
Requirements
What you’ll need- Previous demonstrable role(s) as a Staff, Principal, or Senior Engineer (or equivalent Research Scientist) in a Frontier AI Lab with a proven track record of leading complex, end-to-end AI/ML projects from conception to production.
- Preferably PhD (or equivalent research experience) in Machine Learning, Computer Science, or a related field, preferably with a strong publication record (e.g., NeurIPS, ICML, ICLR) in Computer Science.
- Deep theoretical and practical expertise in Agentic AI and proven experience building, scaling, and shipping solutions involving foundation models (LLMs/VLMs).
- (Bonus Skills) Practical experience applying Reinforcement Learning to systems built on Large Language Models (LLMs).
- Experience with distributed systems or cloud computing, preferably in AWS.
- Familiarity with building complex simulation environments for agent training.
- Experience with LLM training or fine-tuning.
- Experience developing large-scale evaluation and benchmarking systems for AI models.
- Experience in an agentic framework (e.g., LangChain, AutoGen, CrewAI, OpenAI SDK).
- Expertise in system architecture, instrumentation, observability, and monitoring for complex, high-performance systems.
Benefits
Comp & perks- Join the exciting journey of shaping the future of AI, and be part of the early days of one of the hottest AI startups.
- Collaborate with a fun, dynamic, and multicultural team, working alongside world-class AI talent in a highly collaborative environment.
- Enjoy a competitive salary.
- Unlock opportunities for professional growth, continuous learning, and career development.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningComplex System DesignAlgorithm DevelopmentBenchmarking SystemsSimulation Environment BuildingCode ReviewTechnical InnovationTechnical Debt ManagementAI/ML Project LeadershipFoundation Models
Soft Skills
MentoringCollaborationTechnical Excellence
Certifications
PhD in Machine LearningPhD in Computer Science